ML Solution Engineer (Asset Management & Reliability)

YOKOGAWA ENGINEERING ASIA PTE LTD; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Yokogawa Engineering Asia PTE LTD seeks a seasoned ML engineer to develop and deploy predictive maintenance and asset-management solutions for industrial assets. You will work with sensor and process data to build ML models and demonstrate results to customers.

This role emphasizes collaboration with the Centre of Excellence and customer engagement, applying reliability methods such as MTBF/RCA and exploring Generative AI to enhance asset performance.

Qualifications

• Bachelor's degree in CS/DS or related field.
• 5+ years in ML, data analytics or reliability engineering.
• Experience with Python and ML libraries.
• Familiarity with industrial datasets and time-series data.
• Knowledge of RCM, FMEA, RCA/RCFA is advantageous.
• Strong analytical and client-facing skills.

Responsibilities

• Develop ML models for industrial asset management.
• Evaluate models and improve accuracy.
• Analyse equipment and process data to detect patterns.
• Engage with customers to translate requirements.
• Present findings and propose ML-enabled solutions.
• Collaborate with CoE and regional teams.

Skills

Python

ML techniques

Time-series analysis

Predictive maintenance

Data analytics

Problem solving

Communication skills

Education

Bachelor's Degree in Computer Science / Data Science

Tools

Pandas

Scikit-learn

NumPy

Matplotlib

Job description

Key Responsibilities

Machine Learning & Predictive Solutions

• Develop, evaluate and deploy machine learning and predictive models for industrial asset management applications.
• Apply machine learning techniques such as regression, classification, time-series analysis, anomaly detection and other relevant modelling approaches.
• Develop predictive maintenance and equipment health monitoring solutions for early detection of equipment degradation and potential failures.
• Analyse historical and real-time equipment, sensor and process data to identify patterns, anomalies and failure indicators.
• Evaluate model performance and continuously improve model accuracy and reliability.
• Explore the application of emerging AI technologies, including Generative AI, LLM and RAG, to enhance asset management solutions.

Asset Management & Reliability

• Apply reliability engineering knowledge to support asset performance and predictive maintenance solutions.
• Analyze asset performance using reliability indicators such as MTBF, MTTR, equipment availability and other relevant metrics.
• Apply methodologies such as Reliability Centered Maintenance (RCM), Failure Mode and Effects
• Analysis (FMEA) and Root Cause Analysis (RCA/RCFA) where applicable.
• Work with asset and maintenance data to identify reliability risks and opportunities for performance improvement.
• Support digitalization initiatives involving Asset Operations Management (AOM), Asset Performance Management (APM), condition monitoring and predictive maintenance.

Solution Development & Customer Engagement

• Engage with customers to understand their asset management, reliability and operational challenges.
• Gather and analyse customer requirements and translate them into appropriate ML and digital solution approaches.
• Conduct technical discussions, workshops and solution demonstrations with customers.
• Support solution scoping, feasibility studies, proof-of-concept (PoC) activities and technical proposal development.
• Present analytical findings, ML model results and solution recommendations to customers and key stakeholders.
• Support the implementation and delivery of ML-enabled asset management solutions.

Centre of Excellence & Collaboration

• Work closely with other domain specialists to develop and enhance asset management solutions.
• Provide technical and domain expertise in machine learning, predictive maintenance and asset reliability.
• Contribute to the development of reusable ML models, methodologies, use cases and best practices within the Asset Management CoE.
• Evaluate emerging AI/ML technologies and identify opportunities for application within industrial asset management.
• Support knowledge sharing and capability development across regional teams and stakeholders.

Requirements

• Bachelor's Degree in Computer Science, Data Science, or a related engineering/technical discipline.
• At least 5 years of relevant experience in machine learning, data analytics, reliability engineering, asset management, predictive maintenance or industrial digital solutions.
• Good understanding of machine learning techniques, statistical analysis and predictive modelling.
• Experience with Python and relevant machine learning/data analytics tools and libraries.
• Experience working with industrial equipment, machinery, sensor, process or time-series data would be advantageous.
• Knowledge of asset reliability and maintenance methodologies such as RCM, FMEA, RCA/RCFA, MTBF and MTTR would be advantageous.
• Exposure to Asset Operations Management (AOM), Asset Performance Management (APM), IIoT,
• Digital Twin or condition monitoring technologies would be an advantage.
• Knowledge or experience in Generative AI, LLM, RAG or other emerging AI technologies would be an added advantage.
• Strong analytical and problem-solving skills with the ability to translate business and operational requirements into technical solutions.
• Good communication and presentation skills with the ability to engage customers and collaborate effectively across multidisciplinary teams.